Abstract : We have developed a working prototype of a grid-based global event detection system based on waveform correlation. The algorithm comes from a long-period detector (Shearer, 1994) but we have recast it in a full matrix formulation which can reduce the number of multiplications needed by better than two orders of magnitude for realistic monitoring scenarios. The reduction is made possible by eliminating redundant multiplications in the original formulation. In the matrix formulation, all unique correlations for a given origin time are stored in a correlation matrix (C) which is formed by a full matrix product of a Master Image matrix (M) and a data matrix (D). The detector value at each grid point is calculated by following a different summation path through the correlation matrix. The Master Image is a critical component in the detection system because it determines how the data contribute to the detector output at each grid point. Master Images can be derived either empirically or synthetically. Ultimately we will use both types, but for our preliminary testing we have used synthetic Master Images because their influence on the detector is easier to understand. We tested the system using the matrix formulation with continuous data from the IRIS (Incorporate Research Institutes for Seismology) broadband global network to monitor a 2 degree evenly spaced surface grid with a time discretization of 1 sps; we successfully detected the largest event in a two hour segment from October 1993. Both space and time resolution results are encouraging: the output at the correct gridpoint was at least 33% larger than at adjacent grid points, and the output at the correct gridpoint at the correct origin time was more than 500% larger than the output at the same gridpoint immediately before or after.